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DOE OSTI2025

Quantum Computing Strategy 2026

Quantum computing (QC) is a rapidly maturing technology with the potential for revolutionary impacts on stockpile stewardship science and national security. Recent developments in fault-tolerant architectures have compressed vendor roadmaps, and predictions of a production-ready quantum computer by the mid-2030s are becoming increasingly credible. This strategy provides a roadmap for integrating QC into the Advanced Simulation and Computing (ASC) program by investing in four strategic focus areas: 1. Develop Capabilities in Mission-Relevant Quantum Applications: ASC will prioritize developing quantum-ready applications in mission areas that have shown significant promise for quantum advantage, including simulations of materials in extreme environments, nuclear dynamics, solving linear and nonlinear partial differential equations, and uncertainty quantification. These applications directly support stockpile stewardship science and modernization objectives. 2. Conduct R&D in Algorithms, Software, and Hardware: Sustained research into quantum algorithms, robust software tools, and quantum hardware is essential. ASC will develop efficient quantum algorithms; invest in quantum compilers, debuggers, and performance tools; and explore specialized quantum hardware tailored to NNSA’s unique requirements. 3. Engage with Vendors and Partners: Early and active collaboration with commercial quantum hardware vendors and academic partners is critical. Through testbeds, co-design agreements, and quantum demonstration facilities, ASC will influence hardware design, gain early access to emerging technologies, and ensure that quantum platforms evolve to meet mission needs. 4. Build Knowledge, Experience, and Workforce: Expanding and upskilling the quantum-trained workforce is essential to long-term success. This includes hiring, internal training, university outreach, and postdoctoral support to ensure ASC maintains the expertise required to operate, program, and integrate quantum systems as they become available. While quantum computing will never replace classical computing, it has the potential to solve certain problems with speed and accuracy that would be unachievable using any conceivable classical high-performance computing (HPC) system. By investing strategically in QC, ASC will help propel the emergent QC industry, maintain U.S. technological leadership, ensure mission readiness, and position itself to rapidly adopt quantum technologies as they mature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Fuel cells for single-aisle regional aircraft: System configuration, performance and cost

A hydrogen fuel cell propelled electric aircraft can compete with incumbent turbofan technologies for single-aisle regional aircraft by coupling design of stack, air handling, thermal management, propulsion, and airframe to optimize performance. The stack operates at 95°C to facilitate heat rejection during take-off and below 75°C during cruise to extend lifetime and is oversized to satisfy power requirements at end of life. A multi-stage turbocompressor with a compression ratio >10 is selected to reach high stack power density at 11,300-m cruise altitude. The propulsion system is configured to accommodate air handling within the core duct, an inclined heat exchanger in the outer duct to limit the nacelle size, and variable area nozzles to independently control mass flows through the core and bypass ducts. The airframe is modified for maximum lift coefficient and longer balanced field length for dramatically reduced thrust during take-off, and the fuselage is stretched by 20% to store liquid hydrogen (LH 2 ). Modularization of power systems promotes safety in one engine inoperative scenarios and allows reaching specific power metrics for stack, balance-of-plant and fuel cell system (FCS), necessary for acceptable take-off weight. In conclusion, cost parity requires increase in FCS lifetime, LH 2 cost reduction, and improved FCS specific power.

Catalyst durability↗

DOE OSTI2020

X-ray diffraction at the National Ignition Facility

We report details of an experimental platform implemented at the National Ignition Facility to obtain in situ powder diffraction data from solids dynamically compressed to extreme pressures. Thin samples are sandwiched between tamper layers and ramp compressed using a gradual increase in the drive-laser irradiance. Pressure history in the sample is determined using high-precision velocimetry measurements. Up to two independently timed pulses of x rays are produced at or near the time of peak pressure by laser illumination of thin metal foils. The quasi-monochromatic x-ray pulses have a mean wavelength selectable between 0.6 Å and 1.9 Å depending on the foil material. The diffracted signal is recorded on image plates with a typical 2 θ x-ray scattering angle uncertainty of about 0.2° and resolution of about 1°. Analytic expressions are reported for systematic corrections to 2 θ due to finite pinhole size and sample offset. A new variant of a nonlinear background subtraction algorithm is described, which has been used to observe diffraction lines at signal-to-background ratios as low as a few percent. Variations in system response over the detector area are compensated in order to obtain accurate line intensities; this system response calculation includes a new analytic approximation for image-plate sensitivity as a function of photon energy and incident angle. This experimental platform has been used up to 2 TPa (20 Mbar) to determine the crystal structure, measure the density, and evaluate the strain-induced texturing of a variety of compressed samples spanning periods 2–7 on the periodic table.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Probing turbulence with lasers and coherent light sources

Fluid and plasma turbulence consists of multi-scale interactions where the energy of the large scales is dissipated through a cascade to the smallest scales set by the microphysics. Turbulence and mixing can be detrimental to system performance, such as in inertial confinement fusion (ICF). Our current understanding of turbulence is mostly derived from studies of incompressible flows, where the intermediate-scaled eddies in the “inertial range” bridge the transfer of energy from the large eddies to the dissipative range, and that there is no communication between eddies of disparate scales. Whether a similar cascade process exists for compressible turbulence, however, remains to be seen; experiments have been limited by diagnostics and supersonic flow facilities. This project has focused on the development of techniques to measure the evolution of flows and microphysical transport properties using particle tracking velocimetry. This project has investigated three main areas: (1) particle-tracking experiments with x-ray radiography, (2) shock-particle interactions, (3) quantifying viscous effects on particles in shocked flows.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Evaluation of Drilling Performance at The Geysers with Machine Learning Methods Using Geologic Data

A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

New Design Yields Robust Large-Area Framing Camera

Manuscript to be submitted to Review of Scientific Instruments to determine interest in possible publication. Website: https://aip.scitation.org/rsi/authors/manuscript High-speed imaging is a core diagnostic capability for the Nevada National Security Site (NNSS). Data from experiments that measure the behavior of materials under extreme conditions are essential for the stockpile mission. For example, during subcritical experiments, materials that are shocked and compressed under high-explosive drive change behavior rapidly over timescales measured in millionths or even billionths of a second. Characterizing and understanding this behavior requires radiographic and/or optical imaging to track motion on these timescales. Framing cameras provide a discrete series of images over a short period of time in a manner that mimics a high-speed movie camera, but with individual shutter times that must be extremely short in order to capture freeze-frame images of rapidly evolving phenomena such as high-explosive shock-driven ejecta, dynamic compression of metals, and high-velocity fluid flow.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Characterizing Stress Orientations in Southern Kansas

We repot that Induced seismicity predominantly occurs along faults that are optimally oriented to the local principal compressive stress direction, and the characterization of these stress orientations is an important component of understanding seismic hazards. The seismicity rate in southern Kansas rapidly increased in 2013 primarily due to the disposal of large volumes of wastewater into the Arbuckle Group. Previously, local stress orientations in this area were poorly constrained, which limited our understanding of the complex faulting and diverse earthquake mechanisms in this region. We use shear-wave splitting and focal mechanism inversion techniques to create multiple, independent estimates of maximum horizontal stress directions (S Hmax ) and their spatial variation across the study area. We then create an integrated model of stress orientations for southern Kansas and northern Oklahoma using our local results in conjunction with previous, regional stress orientation estimates. We find that S Hmax in both southern Kansas and central Oklahoma exhibits an east-northeast (~N78°E) orientation, and these regions bound a northeast (~N59°E) rotation within a ~20km area in northern Oklahoma near the Nemaha ridge.

58 GEOSCIENCES↗

DOE OSTI2024

Tailoring Nanoporous Silica and Natural Straw Structural Insulation Composites

Nanoporous silica exhibits ultralow thermal conductivity as a result of its nanoscale pore size, high pore volume, and specific surface area. In this study, we report the self-assembled surfactant-templated synthesis of nanoporous silica, which is integrated with cellulose fibers derived from natural straw to manufacture high thermal insulation and mechanically robust nanocomposites. The nanocomposite shows a low thermal conductivity of 22.5 mW/(m·K), a compressive modulus of 0.93 MPa, and a hydrophobicity with a water contact angle of 125°. Moreover, we observed a marked reduction in water absorption capacity and a carbon footprint of 0.21 kg CO 2 equiv/kg. Here, this study provides a pathway toward the development of nanoporous structural insulation materials for energy-efficient building applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Creep deformation and cavitation in an additively manufactured Al-8.6Cu-0.4Mn-0.9Zr (wt%) alloy

Creep deformation and cavitation were investigated at 300 ºC in both tension and compression for an additively manufactured Al-8.6Cu-0.5Mn-0.9Zr (wt%) alloy in the as-fabricated state and after various aging treatments (aging at 300 °C/200 h or 350 °C/24 h and overaging at 400 °C/200 h). Creep mechanisms at 300 °C were determined by relating the measured creep response to corresponding microstructural and X-ray computed tomography observations. In compression, alloys in the as-fabricated and two aging conditions exhibited similarly high creep resistance. Overaging (400 °C/200 h) led to substantial coarsening of intragranular θ-Al2Cu precipitates and an expected drop in their Orowan strengthening contribution. In tension, minimum strain rates comparable to those in compression were obtained at any given stress; however, upon accumulation of some plastic strain in the matrix, creep cavities started to form, leading to accelerated tertiary stage creep deformation and rupture. Cavitation occurred exclusively along melt pool boundaries due to locally enhanced diffusion enabled by (i) large grain-boundary area in adjacent fine-grained zones and (ii) localization of creep strain in nearby heat-affected zones. Although cavity growth was initially diffusion-controlled, its rate was determined by matrix creep rate, consistent with constrained cavity growth mechanisms. This study reveals how microstructural complexities induced by the additive manufacturing process affect the creep and cavitation behavior of Al-Cu-Mn-Zr alloys. The underlying creep and cavitation mechanisms uncovered in this study point to pathways that improve the high-temperature properties of additively manufactured alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites

With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. Here, this study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Elucidating size effects on the yield strength of single-crystal Cu via the Richtmyer–Meshkov instability

Capturing the dynamic response of a material under high strain-rate deformation often demands challenging and time consuming experimental effort. While shock hydrodynamic simulation methods can aid in this area, a priori characterizations of the material strength under shock loading and spall failure are needed in order to parameterize constitutive models needed for these computational tools. Moreover, parameterizations of strain-rate-dependent strength models are needed to capture the full suite of Richtmyer–Meshkov instability (RMI) behavior of shock compressed metals, creating an unrealistic demand for these training data solely on experiments. Herein, we sweep a large range of geometric, crystallographic, and shock conditions within molecular dynamics (MD) simulations and demonstrate the breadth of RMI in Cu that can be captured from the atomic scale. In this work, yield strength measurements from jetted and arrested material from a sinusoidal surface perturbation were quantified as Y RMI = 0.787 ± 0.374 GPa, higher than strain-rate-independent models used in experimentally matched hydrodynamic simulations. Defect-free, single-crystal Cu samples used in MD will overestimate Y RMI , but the drastic scale difference between experiment and MD is highlighted by high confidence neighborhood clustering predictions of RMI characterizations, yielding incorrect classifications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Residual strain-stress in manganese steel railway crossing determined by synchrotron and laboratory X-rays

The residual stress within the nose of a worn manganese steel railway crossing was investigated. The macroscopic residual stress at the running surface was determined in areas with and without visual faults by laboratory X-rays. To get information about the penetration depth of the residual stress, synchrotron X-ray diffraction was used to map the distribution of local (within grain) residual strains below the running surface. The laboratory X-ray measurements revealed significant compressive stress on the rail running surface. The synchrotron measurements documented that local strains exist through the entire depth investigated. Relations between residual stress, plastic deformation, and crack formation are analysed, leading to a discussion on potentials/limitations of the two experimental methods for investigations of residual stress in large engineering components.

differentia aperture synchrotron diffraction↗

DOE OSTI2020

Review—Development of Highly Active and Durable Hybrid Compressive Platinum Lattice Catalysts for Polymer Electrolyte Membrane Fuel Cells: Mathematical Modeling and Experimental Work

This review provides a comprehensive overview on the development of highly active and durable platinum catalysts with ultra-low Pt loadings for polymer electrolyte membrane fuel cells (PEMFCs) through a combined mathematical modeling and experimental work. First, simulation techniques were applied to evaluate the validity of the Tafel approximation for the calculation of the mass activity (MA) and specific activity (SA). A one-dimensional agglomeration model was developed and solved to understand the effects of exchange current density, porosity, agglomerate size, Nafion ® film thickness, and Pt loading on the MA and SA. High porosity (> 60%) and agglomerations at high Pt loadings cause the loss of the Tafel approximation and consequently the decrease in MA and SA. A new structure parameter was introduced to estimate the real porous structure using the fractal theory. The volumetric catalyst density was corrected by the fractal dimension (measured by Hg porosimetry), which gave a good agreement with the experimental values. The loading-dependent Tafel equation was then derived, which contains both the utilization and the non-linear scaling factor. Second, activated carbon composite support (ACCS) with optimized surface area, porosity, pore size, and pore size distribution was developed. The hydrophilic/hydrophobic ratio, structural properties (amorphous/crystalline ratio), and the number of active sites were optimized through metal-catalyzed pyrolysis. Stability of ACCS and Pt/ACCS were evaluated using an accelerated stress test (AST). The results indicated that Pt/ACCS showed no significant loss of MA and power density after 5,000 cycles at 1.0–1.5 V, while the commercial Pt/C catalysts showed drastic losses of MA and power density. Finally, monolayers of compressed Pt (core–shell-type Pt 3 Co 1 ) catalysts were structured by diffusing Co atoms (previously embedded in ACCS) into Pt. Compressive Pt lattice (Pt * ) catalysts were synthesized through an annealing procedure developed at the University of South Carolina (USC). The Pt * /ACCS catalyst showed high initial power density (rated) of 0.174 g Pt kW −1 and high stability (24 mV loss) at 0.8 A cm −2 after 30,000 cycles (0.6–1.0 V). The outstanding performance of Pt * /ACCS is due to the synergistic effect of ACCS and compressive Pt * lattice.

25 ENERGY STORAGE↗

DOE OSTI2020

Single-pulse (100 ps) extended x-ray absorption fine structure capability at the Dynamic Compression Sector

Determining real-time changes in the local atomistic order is important for a mechanistic understanding of shock wave induced structural and chemical changes. However, the single event and short duration (nanosecond times) nature of shock experiments pose challenges in obtaining Extended X-ray Absorption Fine Structure (EXAFS) measurements - typically used for monitoring local order changes. Here, we report on a new single pulse (~100 ps duration) transmission geometry EXAFS capability for use in laser shock-compression experiments at the Dynamic Compression Sector (DCS), Advanced Photon Source. We used a flat plate of highly oriented pyrolytic graphite (HOPG) as the spectrometer element to energy disperse x-rays transmitted through the sample. It provided high efficiency with approximately 15% of the x-rays incident on the HOPG reaching an x-ray area detector with high quantum efficiency. This combination resulted in good signal-to-noise ratio (~10 3 ), energy resolution of ~10 eV at 10 keV, EXAFS spectra covering 100s of eV, and good pulse to pulse reproducibility of our single pulse measurements. Ambient EXAFS spectra for Cu and Au are compared to reference spectra, validating our measurement system. Comparison of single pulse EXAFS results for ambient and laser shocked Ge(100) shows large changes to the local structure of the short lived state of shocked Ge. The current DCS EXAFS capability can be used to perform single pulse measurements in laser shocked materials from approximately 9-13 keV. These EXAFS developments will be available to all users of the DCS.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Project DarkStar: Vision for LLNL in 2030

DarkStar was a Strategic Initiative (FY2021-FY2024) to investigate applications of Artificial Intelligence (AI) and Machine Learning (ML) to scientific problems of complex hydrodynamics, shockwave physics and energetic materials. The research focused on physics and engineering design as a process that can be tremendously accelerated through merging AI with advanced physics simulation on exascale-class platforms, and to experimentally validate this revolutionary new approach through dynamic materials campaigns. A central thread of scientific inquiry was in the application of AI to enable human understanding of how to control hydrodynamic instability (which has impacts to areas such as inertial confinement fusion) via engineering features and time-dependent sources. Motivated by an unfinished line of research started by Dr. Johnny von Neumann, AI-enabled simulation approaches were developed that allowed DarkStar researchers to uncover several ground-breaking discoveries regarding hydrodynamic instability, including how to completely suppress Richtmyer-Meshkov instability (RMI). These S&T discoveries, along with other advances, have shown the way for an entirely new approach to time-dependent problems known as inverse design – the idea that complex systems can be developed directly from a final state that is to be achieved and resolve the initial design via satisfying several constraints simultaneously via AI/ML. Through experimental campaigns conducted across a wide range of facilities in the NNSA complex (the High Explosive Application Facility at LLNL, the Dynamic Compression Sector/Advanced Photon Source at Argonne National Lab, and Special Technologies Laboratory at MSTS) the radical new AI/ML approach to engineering complex material dynamics was verified, establishing a new field of study within the realm of shock physics. As advanced manufacturing capabilities continue to develop, the great importance of inverse design as a means to apply that technology effectively for NNSA missions will feature prominently over this decade. DarkStar has positioned NNSA as a world-leader in this newly emerging cross-disciplinary area of AI methods for advanced physics simulation and pioneered multiple novel approaches that have enabled the broader scientific community. By allowing us to see past the horizon, to 2030 and beyond, DarkStar has illuminated the vast potential of AI/ML to impact a wide range of new national security missions and, consequently, multiple areas of further research have already emerged across the NNSA and DOD complex.

42 ENGINEERING↗

DOE OSTI2026

Optimization techniques in self-similar compressible flow

We investigate the one-dimensional (1D) inviscid compressible flow equations for an ideal gas through the lens of optimization techniques. It is the case that, to our knowledge, optimization analysis applied to the so-called “linear velocity” solutions of the Euler compressible flow equations has not been previously conducted. Through both gradient-based and variational techniques, new variants of well-studied flow scenarios, i.e., self-similar, 1D, linear velocity solution class to idealized inviscid compressible flow equations, are determined, as encoded in both the kinematic and thermodynamic properties of this self-similar solution class. With the kinematics of the said solutions being driven by a self-similar “scale radius” and the thermodynamics being driven separately through the appearance of an arbitrary function, a myriad of new solution classes is possible. Acting as a guide to more realistic physical circumstances as well as discovery, it is the hope that the presented cases serve as the framework for future investigations into the intersection of self-similarity and optimization techniques. Fields of study that may find this work to be of interest include aerodynamic design, flow control, inertial confinement fusion, physics-informed neural networks, and other related areas of interest.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

Salathe, Marco↗

DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

Prins, Nicholas↗